JB, SMC, and RCG were supported by NIH R01MH1006674 to SMC and NIH R01EB021711 to RCG. CMJ and BHS were supported by NSF Ideas lab project on &#34;Cracking the olfactory code&#34; to BHS.&#160;We thank Kyle Steinmetz, Taryn Oboyle, and Rachael Halby for their assistance in conducting this research.

1. Catch and Harness Honey Bees
<ol>
	<li>Follow the Protocol steps 1 through 3.1.1 of Smith and Burden59.</li>
</ol>
2. Preparing the Animal Harness and Video Camera
<ol>
	<li>Hide the legs by applying tape over the top of the harness tube, visually inspecting that legs cannot be seen moving from the top.</li>
	<li>Restrain the head by applying heated wax to the back of the insect head. Visually inspect that the head is fixed and not moving. At this point, the antennae and the mandibles should be the only appendages free to move.</li>
	<li>Maximize contrast between the antennae and the video background by placing a white sheet of paper underneath the insect harness. To minimize the need to later adjust the camera, mark the location of the insect harness on the paper, and then place new individuals at the same location.</li>
	<li>Fix the camera position using a tripod or a webcam holder to place the camera above the insect&#39;s head. Using the camera software, preview the video, and zoom in to magnify the head image, allowing for a ~20 - 30% clearance on all sides of the video.
	<ol>
		<li>Ensure that the only moving objects in the camera view are the antennae or the proboscis/mandibles and reposition the camera or the animal if necessary. 
		NOTE: SwarmSight checks for movement in pixels surrounding the head. Extraneous motion in the immediate vicinity of the head caused by objects such as legs, shadows, fans, or humans may confuse the software, and introduce additional noise.</li>
	</ol>
	</li>
	<li>Minimize antenna shadows by adjusting ambient lighting. 
	NOTE: The software can tolerate some shadows, but for best results, they should be kept to a minimum.</li>
	<li>Prevent automatic camera exposure adjustments by using camera shutter speed software to keep the camera exposure time constant throughout the video. Using the software, adjust the shutter speed to maximize contrast (video scene not too light or too dark), by adjusting the &#39;Exposure slider&#39; under &#39;Webcam Settings.&#39; 
	NOTE: The above instructions are specific to the webcam and software used. These will need to be adapted if other webcams are used.</li>
	<li>Place the odor delivery source and ensure that it does not obstruct the camera view by inspecting the camera video feed. Ensure that a vacuum source is placed on the opposite side to remove the stimulus odors.</li>
	<li>Place an LED, or some other visual indicator that changes brightness to indicate odor delivery, within the camera view. 
	NOTE: The LED brightness value is saved by the software and can be used to determine the exact frames when the odor delivery begins and ends.</li>
</ol>
3. Film Each Individual under Experimental Conditions
<ol>
	<li>Film each individual insect and test condition in separate video files by either recording each individual test combination separately or using video editing software to split a long video file into smaller files. 
	NOTE: The software requires the user to locate the position of the head in each video, and for the head to remain fixed. If the head moves, additional noise will be introduced. The Batch Processing feature of SwarmSight allows the user to rapidly set the location of the head for multiple videos and assumes that the insect head remains fixed for the duration of each video file. Instructions on how to split long video files can be found online60.</li>
</ol>
4. Video Analysis
<ol>
	<li>Download and install the &#39;Antenna Tracking&#39; module by following the steps provided online58. 
	NOTE: Video tutorials describing how to use the software are available on the website as well.</li>
	<li>Open a video file showing a filmed animal by using the &#39;Browse&#39; button.</li>
	<li>Positioning the antenna sensor and treatment sensor
	<ol>
		<li>Once the video loads, position the rectangular &#34;Antenna Sensor&#34; widget over the animal&#39;s head, using the rotation and scale icons to align the widget with the head (see Figure 1D for example).</li>
		<li>Position the circular &#34;Treatment Sensor&#34; widget over the LED that indicates when the odor or stimulus is being presented. 
		NOTE: The Treatment Sensor will record the brightness value of the pixel at the center of the widget for every frame.</li>
	</ol>
	</li>
	<li>Starting video processing
	<ol>
		<li>Press the &#34;Play&#34; button (black triangle) in the bottom left corner to start the analysis of the frames. 
		NOTE: The detected likely antenna and proboscis points will be highlighted yellow. The yellow rings will show the location of the tips of the appendages. The angles (where 0 is directly in front of the animal) of the antenna and the proboscis extension length will be shown in the &#34;Model&#34; widget in the lower left corner (see Figure 1D). The &#34;Dominant Sectors&#34; widget in the lower right corner will show the relative intensity of the five 36-degree sectors where the most antenna points have been detected. The darkest sectors contain the most points, while the lightest have the fewest. The sector number (1 - 5) with the most points will be shown in the lower corners of the widget (see Figure 1D).</li>
	</ol>
	</li>
	<li>Adjusting filter thresholds and adding exclusion zones
	<ol>
		<li>To change the sensitivity of the filters, adjust sliders in the &#34;Filters&#34; section, on the right panel. 
		NOTE: Depending on the lighting conditions and general movement speed of the appendages, different filter sensitivities will be optimal. The user can find the optimal values by adjusting the values and observing the highlighted areas in the Antenna Sensor widget. When an ideal set of sensitivities is found, only the appendages will be highlighted. It is recommended to fast forward to other parts of the video to ensure the filter sensitivities are optimal there, too.</li>
		<li>Optionally, to ignore extraneous objects, on the right panel, expand the &#34;Antenna Sensor&#34; section, click the &#34;Add Exclusion Zone&#34; button (see Figure 1D), and click on a set of points to form a red polygon, the contents of which will be ignored by the software. 
		NOTE: If the video contains extraneous motion, and the motion is within the Antenna Sensor widget&#39;s zone (e.g., moving legs, strong shadows, lab equipment, etc.), the software may mistake it for appendage movement. The extraneous objects can be ignored by drawing red polygons or &#34;Exclusion Zones.&#34;&#160;Anything inside a red polygon will not be used for tracking.</li>
	</ol>
	</li>
	<li>Saving results
	<ol>
		<li>Once the filters and widgets have been set up, stop the video, restart it from the beginning, and play it to the end. 
		NOTE: Once the whole video has played, the positions of the appendages for all video frames will be stored in the memory.</li>
		<li>To save the appendage position data to a file, expand the &#34;Save&#34; section on the right, and click the &#34;Save to .CSV&#34; button. Then choose a folder to save the file. 
		NOTE: The &#34;Save to .CSV&#34; button will save the processing results to a .csv file. By default, the user will be offered to save the .csv file in the same folder as the video file and will have a date and time as part of the file name. The resulting .csv file will contain a set of columns that contain information about the position of the appendages, including the antenna angles and dominant sectors, as well as orientation and position of the head. The description of each column is provided online61.</li>
		<li>Optionally, use the Columns(s) and Value(s) fields in the Save section to create an extra column (or more if separated by commas) in the .csv file to record information, such as subject ID or the name of an experimental condition. 
		NOTE: The value in the Column(s) box will appear in the header of the first column and the value in the Value(s) box will be repeated in all rows of the first column.</li>
	</ol>
	</li>
	<li>Batch processing 
	NOTE: The software can process multiple video files in a batch. However, the user must provide the head location information for each video before starting the batch.
	<ol>
		<li>In the right panel, in &#34;Video Files&#34; section, click the &#34;Batch Processing&#34; button to open a window that allows creating a list of video files to be processed sequentially by the software.</li>
		<li>Use the &#34;Add More Video Files to Batch&#34; button to select one or more video files to be included in the batch list.</li>
		<li>Optionally, use the &#34;CTRL&#34; or &#34;SHIFT&#34; keys to select multiple videos that will use the same set of widget parameters. 
		NOTE: Good candidates for parameter reuse are sets of videos of the same animal that has not been moved between different experimental conditions.</li>
		<li>Start setting the widget parameters to be used for the selected videos by clicking the &#34;Set Sensor Positions for Selected&#34; button.</li>
		<li>Adjust parameters in the Antenna Sensor, Treatment Sensor, Filters, or Save sections, and click &#34;Save Parameters to Batch&#34; when done.</li>
		<li>Once the parameters for each video have been selected, start the batch process by clicking the &#34;Start Processing&#34; button. 
		NOTE: The software will load the video files in the order in which they appear in the batch list, process them, and save their corresponding .csv files to the same folder where the video files are located. A progress bar at the top will provide an estimated finish time after the first video has been completed.</li>
	</ol>
	</li>
</ol>

The authors declare that they have no competing financial interests.

The method presented here enables real-time tracking of insect antenna and proboscis movements without requiring special animal preparations or hardware.
Limitations:
Despite these advantages, there are some limitations of the method. These include the requirement that the head of the animal is restricted from movement, the need for the user to select the location and orientation of the animal for each video, the requirement to have access to a Windows computer, and the software&#39;s inability to track movement in three dimensions (3D) and in some visually ambiguous appendage positions described below.
The software requires that the head of the animal is fixed in place and is not moving during the video. This is similar to the preparations of previous work48,49,50,51. It is possible to modify the software to allow automatic detection of head rotations, however, this would consume additional processing time and introduce a new source of error. If the modified software were to detect the head rotation incorrectly, this would affect the antennae angles, as their computation is relative to the head rotation angle. Currently, the user selects the head orientation once per video. This approach, while not without human error, minimizes angle calculation errors when the head is not allowed to move during the video.
The software also requires a Windows 7 (or later) operating system (OS). The goal was to make the software easy to install, setup, and use by users without programming or sophisticated computer administration skills. We decided to target Windows because it is widely available, and in cases where access to it is limited, virtual machines (e.g., VirtualBox, VMware, Parallels) with Windows can be easily created. This choice of OS greatly simplifies software installation through the use of an easy-to-use, command-line-free installer and avoids bugs specific to different OSs.
The software only tracks the position of the appendages in 2D space. Insects are known to move their antenna in 3D, which could mean that important information is lost when only 2D coordinates are measured. While the use of multiple cameras or mirrors could aid in collecting the additional information required for 3D localization, it is possible to compute, with the use of trigonometric relations, an estimated out-of-plane position by assuming that the antennae are single line segments of constant length and only move on one side of the camera plane. For honey bees, this assumption holds true to obtain rough estimates for the position in 3D, but would not necessarily be the case for other species and situations.
The software will not correctly detect the antennae and proboscis tip locations in some ambiguous situations. If an animal moves an antenna so that, in the video, it overlaps an extended proboscis, the software will likely detect the tip of the antenna as the tip of the proboscis. The antenna angle however, will still likely be computed correctly (from the non-overlapping part). Similarly, if the antenna tips move directly above the head of the animal (i.e., not on the sides) then the software might only detect the part of the antenna that is visible outside of the head, or assume the previous location of the antenna, or detect spurious video noise as antenna location. In both situations, even human raters have difficulty discerning the antenna from the proboscis or the head. To mitigate this problem, we recommend applying a 3-frame, symmetric rolling median57 filter to the raw X and Y coordinates produced by the software. This filter removes large transient (single-frame) position fluctuations, and preserves longer antenna position movements. We have found that the 3-frame filter performed better than no filter, while wider filters (e.g., 5, 11, or 15 frames) reduced accuracy. Example R code that uses the filter and a video tutorial can be found online58.
Value as a Scientific Tool:
The availability of a method to rapidly obtain accurate insect appendage movements in a cost-effective manner has the potential to open up new areas of investigation.
Proboscis extension reflex (PER) is a commonly used behavioral response to investigate learning and memory of a variety of insects59. Previous studies have generally relied on a binary extended-or-not measure of PER, although video and electromyographic analyses have shown much more complex topologies to proboscis movements65,66. The method here allows rapid quantification of proboscis movements in high temporal and spatial resolutions.
Insect antenna movements in response to odors are poorly understood. One reason for this is that the antennae tend to move so rapidly that a cost-effective, automated means to obtain antenna movement data has not been available. The method presented here could be used to rapidly obtain antenna movement data for large numbers of insects in a large number of conditions. This could aid, for example, researchers investigating the mapping between antenna movements in response to various stimuli, in particular volatile odors. Using cameras that capture frames at 30 Hz, the software can be used to characterize antennal movement dynamics up to 15 Hz (Nyquist limit). If characterization in higher frequencies is needed, cameras with higher capture rates (e.g., 60 or 120 fps) could be utilized. However, a faster computer may be required to process higher fps videos in real-time. We speculate that classes of odors, and possibly even some individual odors, have characteristic innate antennal movements. If those classes or compounds could be discovered, unknown odors or their class could be detected from antennal movement of untrained insects. If such a mapping exists, then the combination of sufficient antenna movement data and state of the art machine learning algorithms should begin to uncover it. Also, how that mapping changes in response to learning, forms during development, or is disrupted with genetic interventions could offer insight into functions of the olfactory system. Finally, this work could give insight into artificial detection of odors if it reveals optimal sampling methods for odors in complex environments.
Future Work:
Here, we showed that antenna movement data can be rapidly obtained and analyzed: significant behavior responses can be detected from the data generated by our software, and several areas of further investigation were identified.
The time courses of stimulus-elicited antenna angle deviations from and recovery to baseline and any stimulus-conclusion rebound effects and its dependence on odor concentration can be investigated and modeled mathematically. Additionally, any changes of antenna movements induced by appetitive or aversive conditioning also can be assessed with the software.
Better differentiation of odors can also be explored. In this study, both odors, in pure and 35x diluted versions elicited similar responses: the antennae, on average, appeared to rapidly withdraw away from the odor source and return to pre-odor baselines after a few seconds. We speculate that even the diluted versions may have been very strong olfactory stimuli for the honey bees. If true, a broader range of concentrations could be used to determine if the antennal responses differentiate the odors. Additionally, more sophisticated analysis may better reveal differences in antennal movements in response to different odors. We have made the data files used to generate figures in this manuscript available to interested researchers on the SwarmSight website67.
Furthermore, while outside the scope of this manuscript, the software could be extended to process videos of animals placed in chambers with dual mirrors angled at 45&#176; (see Figure 1D for example). This could be used to accurately localize and track the appendages and their movement in 3D space. However, the algorithms for 3D tracking would be required to efficiently: (a) disambiguate between multiple antennae when they are visible in one of the side mirrors, (b) correct for imperfections in mirror angles, and (c) account for distortions due to camera positioning.
Finally, additional gains in position accuracy might be realized via the use of a Kalman filter68, which models and utilizes physical state information such as appendage velocity and acceleration to constrain predicted locations. However, any gains in accuracy should be evaluated against any reductions in speed due to additional computations.
Conclusion:
Many insects use antennae to actively sample volatile compounds in their local environments. Patterns in antennal movements may provide insight into insect odor perception and how it is affected by conditioning, toxic compounds, and genetic alterations. Similarly, proboscis movements have been used to assess odor perception and its modulation. However, rapidly obtaining large quantities of high-resolution appendage movement data has been difficult. Here, a protocol and software is described that automates such task. In summary, we have created and demonstrated how the combination of inexpensive hardware, a common animal preparation, and the open-source software can be used to rapidly obtain high-resolution insect appendage movement data. The output of the software, how it outperforms human raters in speed and accuracy, and how its output data can be analyzed and visualized were shown.

Most arthropods move antennae or other appendage to sample environmental cues and signals in time and space. The animals can use the antennae to navigate their environment by detecting sensory cues such as chemical volatiles and gustatory and mechanical stimuli1,2,3,4. In insects, the antennae contain sensory receptors that bind to chemical volatiles4,5,6 and transmit this signal via olfactory sensory neurons to central brain regions1,7,8,9. The insects can adjust antennae positions to modulate information about incoming odors4,10,11. This modulation facilitates actively informed behavioral responses to odors and their plumes12,13.
Many insects, including Hymenopterans (e.g., honey bees and bumblebees), Lepidopterans (e.g., butterflies), and Dipterans (e.g., flies and mosquitoes), among others, feed by extending their proboscis14,15,16,17,18,19,20,21. Proboscis extension has been reliably used in the past for a variety of learning and memory tasks22,23,24,25,26,27,28,29,30,31. Similarly, quantitative assessment of antennae movement with high temporal and spatial resolution might yield insight into the relationship between the stimulus, the behavior, and internal state of the animal. Indeed previous work has shown how the antennal movements contain a rich amount of information about honey bee tracking of the environment and how the movements change with learning32,33,34,35,36,37,38.
In the last decade, methods for observing animal behavior have been greatly accelerated by advances in high-resolution video cameras, computer processing speeds, and machine vision algorithms. Tasks like animal detection, counting, tracking, and place preference analyses have been aided with sophisticated software that can process videos of animal behavior and extract relevant measures39,40,41,42,43,44,45,46,47.
These technologies have also aided tracking of insect antenna and proboscis movements. It is possible for human raters to use a mouse cursor to manually track the position of the antennae. However, while this method can be accurate, the task is time consuming, and human inattention and fatigue can result in unreliable results. Special equipment and preparation can be used to reduce the need for complex software. For example, one setup used a high-speed camera and painted the tips of the antennae to track the antenna movement48. Users can also be asked to select key-frames of videos to assist the software in detecting the antenna and proboscis location49. Another approach detected the two largest motion clusters to identify antennae, but it does not detect the proboscis location50. Another software package can detect antenna and proboscis locations, but requires about 7.5 s of processing time per frame51, which could be prohibitive for real-time or long-term observation studies. Finally, it might be possible to customize commercial software packages (e.g., EthoVision) to perform the task46, but their licensing and training costs can be prohibitive.
With the method described here, we extended our previous work on motion analysis software41 to track the locations of insect antennae and proboscis with the following goals: (1) no requirement for special hardware or complex animal preparation, (2) frame processing at real-time (30 fps or faster) on a conventional computer, (3) ease of use, and (4) open-source, easily extendable code.
The resulting novel method and open-source software, SwarmSight Appendage Tracking, does not require painting of the antennae tips, can use a consumer web camera to capture videos, and processes video frames at 30-60 fps on a conventional computer (Figure 1). The software takes video files as input. The user locates the position of the insect head in the video and, after processing, a comma separated values (.csv) file is produced with the locations of the antennae and proboscis. The software is capable of reading hundreds of different video formats (including formats produced by most digital cameras) through the use of the FFmpeg library52.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56803/56803fig1.jpg" /> 
Figure 1: Animal Setup and software output. (A) A honey bee forager with its head and body restrained in a harness. (B) Odor source is placed in front of the animal, a video camera is positioned above, and a vacuum source is placed behind the animal. (C) The antenna tip and proboscis variables detected by the SwarmSight software from the video. (D) The user positions the antenna sensor over the animal and adjusts the filter parameters. The software detects the antenna and proboscis positions (yellow rings). <a href="//cloudflare.jove.com/files/ftp_upload/56803/56803fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
First, an insect&#39;s body and its head are restrained in a harness such that the antenna and proboscis movements are easily observed (Figure 1A). An odor source is placed in front of the insect, with a vacuum source placed behind, to remove the odors from the air and minimize potential effects of sensory adaptation (Figure 1B). A conventional web camera is placed above the insect&#39;s head on a tripod. An LED can be positioned within the camera&#39;s view to indicate when the odor is being presented.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56803/56803fig2.jpg" /> 
Figure 2: Antenna coordinate system. X, Y values use the video coordinate system, where top left corner is the origin and X and Y values increase when moving towards the bottom right corner. Angles are expressed in degrees with respect to the front of the head (usually the odor source). A &#34;0&#34; value signifies that the line formed by the antenna flagellum is pointing directly in front of the animal. All angles are positive, except when an antenna points to the opposing direction (e.g., right flagellum points to the left). <a href="//cloudflare.jove.com/files/ftp_upload/56803/56803fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
After filming, the video file is opened with the SwarmSight software, where the user positions the Antenna Sensor widget (Figure 1D, black square) over the head of the insect, and starts the video playback. When the results are saved, the .csv file will contain the X, Y positions of the antenna tips, the antenna angles relative to the front of the head (Figure 2), and the proboscis X, Y position. Additionally, a dominant sector metric is computed for each antenna. The metric shows which of the five 36-degree sectors surrounding each antenna contained the most points deemed likely to be the antennae, and can be useful if the antenna position/angle metrics are not reliable due to noisy or otherwise problematic videos.
Briefly, the software works by using a set of motion filters53 and a relaxed flood fill algorithm54. To find likely antenna points, two filters are used: a 3-consecutive-frame difference filter41,55 and a median-background subtraction56 filter. A color distance threshold filter is used for proboscis point detection. The top 10% of the points of each filter are combined, and a flood fill algorithm that inspects contiguous points with gaps up to 2 pixels (px) locates extreme points. Parallel frame decoding, processing, and rendering pipelines and optimized memory allocation of the filter data flow achieves high performance. The raw x and y coordinate values produced by the software are post-processed with a 3-frame rolling median filter57 (see Discussion). The instructions to download the full source code can be found online58.
Below is a protocol to prepare a honey bee forager for antenna tracking. A similar protocol could be used to track the antenna/proboscis movements of any other insect. In the results section, we describe a sample antenna trace output that is detected by the software, the comparison of the software output to tracking performed by human raters, and assessment of antennae movement in response to five odorants.

<table border="0" cellpadding="0" cellspacing="0" width="938">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:188px;">Insect harness</td>
			<td style="width:95px;">N/A</td>
			<td style="width:161px;">N/A</td>
			<td style="width:495px;">Use materials needed for Protocol sections 1-3.1.1 of Smith &#38; Burden (2014)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:188px;">Odor delivery source</td>
			<td style="width:95px;">N/A</td>
			<td style="width:161px;">N/A</td>
			<td style="width:495px;">Use materials needed for Protocol section 3 of Smith &#38; Burden (2014)</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:188px;">Vacuum source</td>
			<td style="width:95px;">N/A</td>
			<td style="width:161px;">N/A</td>
			<td style="width:495px;">Use materials needed for Protocol section 3 of Smith &#38; Burden (2014)</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:188px;">LED connected to odor delivery source</td>
			<td style="width:95px;">N/A</td>
			<td style="width:161px;">N/A</td>
			<td style="width:495px;">Use materials needed for Protocol section 3 of Smith &#38; Burden (2014)</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:188px;">Low Voltage Soldering Iron</td>
			<td style="width:95px;">Stannol</td>
			<td style="width:161px;">Low Voltage Micro Soldering Iron 12V, 8W</td>
			<td style="width:495px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:188px;">DC Power Supply</td>
			<td style="width:95px;">Tekpower</td>
			<td style="width:161px;">HY152A</td>
			<td style="width:495px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:188px;">White sheet of paper</td>
			<td style="width:95px;">Georgia-Pacific</td>
			<td style="width:161px;">998606</td>
			<td style="width:495px;">Any white sheet of paper can be used as alternative</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:188px;">Tripod</td>
			<td style="width:95px;">AmazonBasics</td>
			<td style="width:161px;">50-Inch Lightweight Tripod</td>
			<td style="width:495px;">Optional</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:188px;">Camera</td>
			<td style="width:95px;">Genius</td>
			<td style="width:161px;">WideCam F100</td>
			<td style="width:495px;">FLIR Flea3 or another camera with manual focus can be used.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:188px;">Camera software</td>
			<td style="width:95px;">Genius</td>
			<td style="width:161px;">N/A</td>
			<td style="width:495px;">Software comes with camera. On MacOS, Photo Booth app can be used to record videos.</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:188px;">Camera shutter speed software</td>
			<td style="width:95px;">Genius</td>
			<td style="width:161px;">N/A</td>
			<td style="width:495px;">Genius camera software allows shutter speed setting. In Mac OS, iGlasses by ecamm can be used instead: http://www.ecamm.com/mac/iglasses/</td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:188px;">Windows Operating System</td>
			<td style="width:95px;">Microsoft</td>
			<td style="width:161px;">Windows 7 Professional</td>
			<td style="width:495px;">Versions 7 or later are compatible. Oracle VirtualBox, Parallels Desktop, or VMWare Fusion can be used to create a Windows virtual machine in MacOS environments.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:188px;">SwarmSight software</td>
			<td style="width:95px;">SwarmSight</td>
			<td style="width:161px;">Appendage Tracking</td>
			<td style="width:495px;">Download from http://SwarmSight.org</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:188px;">R software</td>
			<td style="width:95px;">R Project</td>
			<td style="width:161px;">R 3.4.0</td>
			<td style="width:495px;">Download from: https://cran.r-project.org/bin/windows/base/</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:188px;">R Studio software</td>
			<td style="width:95px;">RStudio</td>
			<td style="width:161px;">RStudio Desktop</td>
			<td style="width:495px;">Download from: https://www.rstudio.com/products/rstudio/download/</td>
		</tr>
	</tbody>
</table>

swarmsight: real-time tracking of insect antenna movements and proboscis extension reflex using a common preparation and conventional hardware

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using the video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames per second (fps) videos, can capture antennal dynamics up to 15 Hz. The software was used to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration.

In the sections below are an example plot of antennae angles produced from the data of the software, comparison of the software accuracy and speed with human raters, and the results of an experiment where honey bee antenna movement is affected by presentation of different odors. R software62,63 was used to perform the analysis and generate the figures. R code for analysis and figure generation as well as video tutorials can be found online58.
Software Output:
Figure 3 shows five randomly selected traces of antenna angles detected by the software from videos of honey bees presented with pure and 35x mineral oil diluted versions of heptanal and heptanol, as well as clean air.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56803/56803fig3.jpg" /> 
Figure 3: Five sample traces of antennae angles detected by SwarmSight. Y-axis shows antenna angle in degrees, where &#34;0&#34; is directly in front of the animal, towards the odor source, with larger values pointing away from the odor source. Heptanol, heptanal, and their 35x mineral oil diluted versions, as well as clean air, were applied during the gray 0 - 3,600 ms windows to single honey bee foragers. Left antenna is marked red, right marked blue. Five random bees, one from each condition, are depicted in the five plots. <a href="https://cloudflare.jove.com/files/ftp_upload/56803/56803fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Software Validation:
To validate that the software can reliably detect the locations of the antennae, antenna positions located by humans were compared with the positions located by the software. Two human raters were asked to locate the antenna and proboscis tips in 425 video frames (~14 s of video). A custom software module recorded the appendage locations marked by the raters, automatically advanced video frames, and recorded the amount of time spent on the task. As an example of correspondence between human- and software-located values, superimposed vertical coordinate traces of one antenna for the software and for the two human detected locations are shown in Figure 4A. The distance between the two raters&#39; marked antenna positions was computed and named &#34;Inter-Human Distance.&#34;&#160;The distance between the antenna location detected by the software and the closest location detected by the human raters was computed and named &#34;Software-Closest Human Distance&#34; (Figure 4B).
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56803/56803fig4.jpg" /> 
Figure 4: Comparison with human raters. (A) Two human raters and SwarmSight located antenna tips in 425 video frames. The frame-by-frame left antenna tip Y coordinates found by the human raters and software are superimposed. (B) Superimposed frame-by-frame disagreement (distance in video pixels) between human raters (orange) and disagreement between software and closest human rater value (black). (C) Human vs. human antenna tip locations (orange) and software vs. human locations (black). (D) Histograms and cumulative distributions (dashed) of human vs. human and software vs. human frame-by-frame disagreement distances. <a href="https://cloudflare.jove.com/files/ftp_upload/56803/56803fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Inter-Human Distance was 10.9 px on average, within 55.2 px in 95% of the frames, and had a maximum value of 81.6 px. The Software-Closest Human Distance was 8.0 px on average, within 18.3 px in 95% of the frames, and had a maximum value of 49.0 px (see distance histograms in Figure 4D, and Figure 4C). 5 px was approximately the width of an antenna. Overall, the Inter-Human Distance was small for the frames at the beginning of the task, and increased in the second half of the task. We suspect this was due to rater fatigue. Meanwhile, Software-Closest Human Distance levels remained constant throughout the task.
Software Speed and Accuracy Comparison with Human Raters:
Humans rated antenna tip and proboscis locations at an average speed of 0.52 frames per second (fps). To estimate human fps, the total number of frames rated by humans (425 each) was divided by the total time they spent on the task (873 s and 761 s). The software rated the frames at 65 fps on average on a Dual-Core Windows 7 PC. Together with high processing speed and accuracy similar to or better than human raters, the software can be expected to perform the work of about 125 human raters per unit of time.
Detection of Antenna Response to Odors:
To demonstrate that the protocol can be used to detect significant behavioral differences in insect movement, we subjected 23 female honey bees to two different odors. Pure heptanal and heptanol, 35x mineral oil dilutions of the two odors, and clean air as the control, were presented each for 4 s (five conditions in total). Videos, as described in the protocol above, were processed with SwarmSight software, and the antenna angles analyzed (Figure 5).
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56803/56803fig5.jpg" /> 
Figure 5: Antenna angle means and density heat maps for five odor conditions. (A) Heat maps showing antenna angle density before, during (darker middle region), and after administration of heptanol, air, and heptanal odorants to female honey bees (n = 23). Black curves are per-frame average antenna angles (both antennae). Horizontal lines are pre-odor mean (baseline) angles. Note the cluster of preferred antenna locations (red cluster in bottom plot) away from the odor source for pure odor conditions, and corresponding changes to the mean antenna angle. Also note the &#34;rebound&#34; cluster after odor conclusion and its apparent onset dependence on odor concentration (see cluster location in the other four plots). Density heat map color scale is arbitrary but uniform across all conditions. (B) Mean angle change from pre-odor mean (error bars S.E.M). Except for air, all mean changes were significant (t-test p &#60;0.05). <a href="https://cloudflare.jove.com/files/ftp_upload/56803/56803fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Video frames from 9 s segments of the video consisting of 3 s before the odor onset, 3.6 s of odor presentation, and 2.4 s after odor conclusion were aligned across all individuals and conditions (300 frames/segment). The per-frame means of both antenna angles of all individuals were computed for each condition and called &#34;Mean Angles&#34; (Figure 5A, black curves). The mean antenna angles of frames before the odor onset across individuals for each condition were computed and called &#34;Pre-Odor Baselines&#34; (Figure 5A, thin horizontal lines).
In all conditions, except control, the mean angles increased from baselines, each peaking once 750 - 1,050 ms after the odor onset (Figure 5A, black curves in 0 - 3,600 ms region). The mean changes from baselines were tested for significance (Figure 5B) by comparing the two-antenna means of individuals at the peak odor-presentation mean angle time of each condition to the baseline mean using a series of 1-sample t-tests (Shapiro normality tests not significant in all conditions). The mean angle change from baseline was 26.9&#176; for pure heptanal (mean peaked at 750 ms after odor onset), 21.1&#176; for 0.2 M heptanal (at 990 ms), 19.6&#176; for pure heptanol (at 1,050 ms), 19.3&#176; for 0.2 M heptanol (at 780 ms), and 3.45&#176; for air control (no peak). In all conditions, except control, the mean angle change from baseline was significant (Holm adjusted p &#60;0.05). We note that the mean angle takes longer to return to baseline in response to pure odorants than to diluted odorants (low-pass filtered mean returned to baseline 3,690 ms after odor onset for pure and at 2,940 ms for diluted heptanol; for heptanal, return times were 4,260 ms for pure and 3,000 ms for diluted versions).
Visualization Using Heat Maps:
To visualize the antenna responses, antenna angle density heat maps for each condition were generated (Figure 5A, blue-red background). Antenna angles across the 10 s video segments for each individual per condition were convolved with a Gaussian kernel (R package MASS, kde2d function64). Blue areas show low densities of antenna angles, while red areas show high densities of antenna angles. The heat map in the bottom plot of Figure 5A for the pure heptanal condition illustrates the antenna behavior.
The map shows that before the odor is presented (t &#60; 0), the antenna angle density is distributed relatively uniformly across all angles. About 1 s after odor onset (t ~1,000 ms), a pair of blue and red clusters appears. In areas shaded red, the antennae were found more frequently than in areas shaded blue. The blue cluster indicates that antennae tended to avoid smaller angles (odor source was located in the direction of 0 degrees), while the red cluster indicates that antennae preferred greater angles (away from odor source). The red cluster gradually disappears as the odor presentation is maintained. Another red, albeit less intense, cluster appears about 1 s after odor conclusion. We name the second red clusters &#34;Rebound Clusters&#34;. Consistent with the mean angle recovery times above, we note that the rebound clusters seem to appear earlier and are less intense for diluted odors than for pure odors.

Watch this Scientific Journal Video about SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware at JoVE.com

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

This protocol describes steps for using the novel software, SwarmSight, for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The free, open-source software processes frames about 120 times faster than humans and performs at better than human accuracy.

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis ...

swarmsight-real-time-tracking-insect-antenna-movements-proboscis

Universidad San Sebastian

Research

JoVE Journal

Neuroscience

8.0K Views.  Arizona State University. This protocol describes steps for using the novel software, SwarmSight, for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The free, open-source software processes frames about 120 times faster than humans and performs at better than human accuracy.

Video: SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Chronic Imaging of Mouse Visual Cortex Using a Thinned-skull Preparation

In vivo imaging using two-photon laser scanning microscopy (2PLSM) allows the study of living cells and neuronal processes in the intact brain. The technique presented here allows the imaging of the same area of the brain at several time points (chronic imaging) with microscopic resolution allowing the tracking of dendritic spines which are the small structures that represent the majority of postsynaptic excitatory sites in the CNS. The ability to clearly resolve fine cortical structures over several time points has many advantages, specifically in the study of brain plasticity in which morphological changes at synapses and circuit remodeling may help explain underlying mechanisms. In this video and supplementary material, we show a protocol for chronic in vivo imaging of the intact brain using a thinned-skull preparation. The thinned-skull preparation is a minimally invasive approach, which avoids potential damage to the dura and/or cortex, thus reducing the onset of an inflammatory response. When this protocol is performed correctly, it is possible to clearly monitor changes in dendritic spine characteristics in the intact brain over a prolonged period of time.

In this video and supplemental material, we show a protocol for chronic in vivo imaging of the intact brain using a thinned-skull preparation.

In vivo imaging using two-photon laser scanning microscopy (2PLSM) allows the study of living cells and neuronal processes in the intact brain. The ...

Behavioural Pharmacology in Classical Conditioning of the Proboscis Extension Response in Honeybees (Apis mellifera)

Honeybees (Apis mellifera) are well known for their communication and orientation skills and for their impressive learning capability1,2. Because the survival of a honeybee colony depends on the exploitation of food sources, forager bees learn and memorize variable flower sites as well as their profitability. Forager bees can be easily trained in natural settings where they forage at a feeding site and learn the related signals such as odor or color. Appetitive associative learning can also be studied under controlled conditions in the laboratory by conditioning the proboscis extension response (PER) of individually harnessed honeybees3,4. This learning paradigm enables the study of the neuronal and molecular mechanisms that underlie learning and memory formation in a simple and highly reliable way5-12. A behavioral pharmacology approach is used to study molecular mechanisms. Drugs are injected systemically to interfere with the function of specific molecules during or after learning and memory formation13-16.
Here we demonstrate how to train harnessed honeybees in PER conditioning and how to apply drugs systemically by injection into the bee flight muscle.

Behavioural Pharmacology in Classical Conditioning of the Proboscis Extension Response in Honeybees Apis mellifera

We demonstrate how to implement a behavioral pharmacology method in an appetitive olfactory conditioning paradigm in honeybees (Apis mellifera) by systemic application of drugs. This method allows investigation of the mechanisms underlying learning and memory formation in a simple and reliable way.

Honeybees (Apis mellifera) are well known for their communication and orientation skills and for their impressive learning capability1,2. Because the ...

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., &lt;1 cm/&lt;1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning1, auditory processing2 and visual-spatial attention3. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function1-5. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user's intentions to enhance or improve communication6 and control7. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform8,9 and the SIGFRIED10 method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation.
Prerequisites and Planning:
Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient's brain, and on relevant factors of the patient's history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure.
In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient's performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital's institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient's endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort.
At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient's eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5th Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment.
Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software8,9. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator.
A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient's electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS).
Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping10.
The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

We present a method for collecting electrocorticographic signals for research purposes from humans who are undergoing invasive epilepsy monitoring. We show how to use the BCI2000 software platform for data collection, signal processing and stimulus presentation. Specifically, we demonstrate SIGFRIED, a BCI2000-based tool for real-time functional brain mapping.

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), ...

A Proboscis Extension Response Protocol for Investigating Behavioral Plasticity in Insects: Application to Basic, Biomedical, and Agricultural Research

Insects modify their responses to stimuli through experience of associating those stimuli with events important for survival (e.g., food, mates, threats). There are several behavioral mechanisms through which an insect learns salient associations and relates them to these events. It is important to understand this behavioral plasticity for programs aimed toward assisting insects that are beneficial for agriculture. This understanding can also be used for discovering solutions to biomedical and agricultural problems created by insects that act as disease vectors and pests. The Proboscis Extension Response (PER) conditioning protocol was developed for honey bees (Apis mellifera) over 50 years ago to study how they perceive and learn about floral odors, which signal the nectar and pollen resources a colony needs for survival. The PER procedure provides a robust and easy-to-employ framework for studying several different ecologically relevant mechanisms of behavioral plasticity. It is easily adaptable for use with several other insect species and other behavioral reflexes. These protocols can be readily employed in conjunction with various means for monitoring neural activity in the CNS via electrophysiology or bioimaging, or for manipulating targeted neuromodulatory pathways. It is a robust assay for rapidly detecting sub-lethal effects on behavior caused by environmental stressors, toxins or pesticides.
We show how the PER protocol is straightforward to implement using two procedures. One is suitable as a laboratory exercise for students or for quick assays of the effect of an experimental treatment. The other provides more thorough control of variables, which is important for studies of behavioral conditioning. We show how several measures for the behavioral response ranging from binary yes/no to more continuous variable like latency and duration of proboscis extension can be used to test hypotheses. And, we discuss some pitfalls that researchers commonly encounter when they use the procedure for the first time.

The Proboscis Extension Response (PER) conditioning protocol, developed for the honey bee (Apis mellifera), provides an ecologically-relevant and easily quantifiable means for studying several different mechanisms of learning in many insect species.

Insects modify their responses to stimuli through experience of associating those stimuli with events important for survival (e.g., food, mates, threats). ...

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Experimental neuroscience is witnessing an increased interest in the development and application of novel and often complex, closed-loop protocols, where the stimulus applied depends in real-time on the response of the system. Recent applications range from the implementation of virtual reality systems for studying motor responses both in mice1 and in zebrafish2, to control of seizures following cortical stroke using optogenetics3. A key advantage of closed-loop techniques resides in the capability of probing higher dimensional properties that are not directly accessible or that depend on multiple variables, such as neuronal excitability4 and reliability, while at the same time maximizing the experimental throughput. In this contribution and in the context of cellular electrophysiology, we describe how to apply a variety of closed-loop protocols to the study of the response properties of pyramidal cortical neurons, recorded intracellularly with the patch clamp technique in acute brain slices from the somatosensory cortex of juvenile rats. As no commercially available or open source software provides all the features required for efficiently performing the experiments described here, a new software toolbox called LCG5 was developed, whose modular structure maximizes reuse of computer code and facilitates the implementation of novel experimental paradigms. Stimulation waveforms are specified using a compact meta-description and full experimental protocols are described in text-based configuration files. Additionally, LCG has a command-line interface that is suited for repetition of trials and automation of experimental protocols.

Closed-loop protocols are becoming increasingly widespread in modern day electrophysiology. We present a simple, versatile and inexpensive way to perform complex electrophysiological protocols in cortical pyramidal neurons in vitro, using a desktop computer and a digital acquisition board.

Experimental neuroscience is witnessing an increased interest in the development and application of novel and often complex, closed-loop protocols, where ...

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor sources is an essential task for organism survival. Insects are well-studied organisms with regard to odor tracking, and their behavioral strategies have been applied to mobile robots for evaluation. This "bottom-up" approach is a fundamental way to develop biomimetic robots; however, the biological analyses and the modeling of behavioral mechanisms are still ongoing. Therefore, it is still unknown how such a biological system actually works as the controller of a robotic platform. To answer this question, we have developed an insect-controlled robot in which a male adult silkmoth (Bombyx mori) drives a robot car in response to odor stimuli; this can be regarded as a prototype of a future insect-mimetic robot. In the cockpit of the robot, a tethered silkmoth walked on an air-supported ball and an optical sensor measured the ball rotations. These rotations were translated into the movement of the two-wheeled robot. The advantage of this "hybrid" approach is that experimenters can manipulate any parameter of the robot, which enables the evaluation of the odor-tracking capability of insects and provides useful suggestions for robotic odor-tracking. Furthermore, these manipulations are non-invasive ways to alter the sensory-motor relationship of a pilot insect and will be a useful technique for understanding adaptive behaviors.

The capability to localize an odor source is necessary for insect survival and is expected to be applicable to artificial odor-tracking. The insect-controlled robot is driven by an actual silkmoth and enables us to evaluate the odor-tracking capability of insects through a robotic platform.

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor ...

Triggering Cell Stress and Death Using Conventional UV Laser Confocal Microscopy

Using a standard confocal setup, a UV ablation method can be utilized to selectively induce cellular injury and to visualize single-cell responses and cell-cell interactions in the CNS in real-time. Previously, studying these cell-specific responses after injury often required complicated setups or the transfer of cells or animals into different, non-physiological environments, confounding immediate and short-term analysis. For example, drug-mediated ablation approaches often lack the specificity that is required to study single-cell responses and immediate cell-cell interactions. Similarly, while high-power pulsed laser ablation approaches provide very good control and tissue penetration, they require specialized equipment that can complicate real-time visualization of cellular responses. The refined UV laser ablation approach described here allows researchers to stress or kill an individual cell in a dose- and time-dependent manner using a conventional confocal microscope equipped with a 405-nm laser. The method was applied to selectively ablate a single neuron within a dense network of surrounding cells in the zebrafish spinal cord. This approach revealed a dose-dependent response of the ablated neurons, causing the fragmentation of cellular bodies and anterograde degeneration along the axon within minutes to hours. This method allows researchers to study the fate of an individual dying cell and, importantly, the instant response of cells-such as microglia and astrocytes-surrounding the ablation site.

Targeted manipulations to cause directed stress or death in individual cells have been relatively difficult to accomplish. Here, a single-cell-resolution ablation approach to selectively stress and kill individual cells in cell culture and living animals is described based on a standard confocal UV laser.

Using a standard confocal setup, a UV ablation method can be utilized to selectively induce cellular injury and to visualize single-cell responses and ...

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control neuroplastic processes and correct abnormal function, or even shift functions from damaged tissue to physiologically healthy brain regions, hold the potential to dramatically improve overall health. Of the current neuroplastic interventions in development, neurofeedback training (NFT) from functional Magnetic Resonance Imaging (fMRI) has the advantages of being completely non-invasive, non-pharmacologic, and spatially localized to target brain regions, as well as having no known side effects. Furthermore, NFT techniques, initially developed using fMRI, can often be translated to exercises that can be performed outside of the scanner without the aid of medical professionals or sophisticated medical equipment. In fMRI NFT, the fMRI signal is measured from specific regions of the brain, processed, and presented to the participant in real-time. Through training, self-directed mental processing techniques, that regulate this signal and its underlying neurophysiologic correlates, are developed. FMRI NFT has been used to train volitional control over a wide range of brain regions with implications for several different cognitive, behavioral, and motor systems. Additionally, fMRI NFT has shown promise in a broad range of applications such as the treatment of neurologic disorders and the augmentation of baseline human performance. In this article, we present an fMRI NFT protocol developed at our institution for modulation of both healthy and abnormal brain function, as well as examples of using the method to target both cognitive and auditory regions of the brain.

The ability to induce and/or control neural plasticity may be critical in future treatments for neurologic disorders and the recovery from brain injury. In this paper, we present a protocol on the use of neurofeedback training with functional magnetic resonance imaging to modulate human brain function.

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control ...

Live-cell Measurement of Odorant Receptor Activation Using a Real-time cAMP Assay

The enormous sizes of the mammalian odorant receptor (OR) families present difficulties to find their cognate ligands among numerous volatile chemicals. To efficiently and accurately deorphanize ORs, we combine the use of a heterologous cell line to express mammalian ORs and a genetically modified biosensor plasmid to measure cAMP production downstream of OR activation in real time. This assay can be used to screen odorants against ORs and vice versa. Positive odorant-receptor interactions from the screens can be subsequently confirmed by testing against various odor concentrations, generating concentration-response curves. Here we used this method to perform a high-throughput screening of an odorous compound against a human OR library expressed in Hana3A cells and confirmed that the positively-responding receptor is the cognate receptor for the compound of interest. We found this high-throughput detection method to be efficient and reliable in assessing OR activation and our data provide an example of its potential use in OR functional studies.

Characterizing the function of odorant receptors serves an indispensable part in the deorphanization process. We describe a method to measure the activation of odorant receptors in real time using a cAMP assay.

The enormous sizes of the mammalian odorant receptor (OR) families present difficulties to find their cognate ligands among numerous volatile chemicals. ...

Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase

Olfactory perception begins with the interaction of odorants with odorant receptors (OR) expressed by olfactory sensory neurons (OSN). Odor recognition follows a combinatorial coding scheme, where one OR can be activated by a set of odorants and one odorant can activate a combination of ORs. Through such combinatorial coding, organisms can detect and discriminate between a myriad of volatile odor molecules. Thus, an odor at a given concentration can be described by an activation pattern of ORs, which is specific to each odor. In that sense, cracking the mechanisms that the brain uses to perceive odor requires the understanding odorant-OR interactions. This is why the olfaction community is committed to &#34;de-orphanize&#34;&#160;these receptors. Conventional in vitro systems used to identify odorant-OR interactions have utilized incubating cell media with odorant, which is distinct from the natural detection of odors via vapor odorants dissolution into nasal mucosa before interacting with ORs. Here, we describe a new method that allows for real-time monitoring of OR activation via vapor-phase odorants. Our method relies on measuring cAMP release by luminescence using the Glosensor assay. It bridges current gaps between in vivo and in vitro approaches and provides a basis for a biomimetic volatile chemical sensor.

Physiologically, odorant receptors are activated by odorant molecules inhaled in the vapor phase. However, most in vitro systems utilize liquid phase odorant stimulation. Here, we present a method that allows real-time in vitro monitoring of odorant receptor activation upon odorant stimulation in vapor phase.

Olfactory perception begins with the interaction of odorants with odorant receptors (OR) expressed by olfactory sensory neurons (OSN). Odor recognition ...